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English(EN) A Quadratic Order Reduction -- Gaussian Process Ordinary Differential Equation framework for the inference of Large Continuous Dynamical Systems

新的高斯过程框架增强动力系统预测能力

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种将高斯过程与二次模型降阶相结合的复杂动力系统预测新框架。该方法旨在提高准确性、数值稳定性和不确定性量化，而这些往往是现有方法面临的挑战。所提出的模型结合了高斯过程常微分方程、二次降阶和球体投影，以在保持稳定性的同时有效地学习潜在动力学。数值实验表明，该框架在准确性和计算效率方面优于扩展动态模式分解等方法。 AI

影响 该框架为复杂系统提供了改进的预测和不确定性量化能力，可能惠及科学研究和工程应用。

排序理由 该集群包含一篇详细介绍新计算框架的学术论文。

在 arXiv stat.ML 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新的高斯过程框架增强动力系统预测能力

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报道来源 [2]

  1. arXiv stat.ML TIER_1 English(EN) · Guglielmo Padula, Michele Girfoglio, Gianluigi Rozza ·

    用于大型连续动力学系统推理的二次阶降阶——高斯过程常微分方程框架

    arXiv:2606.13063v1 Announce Type: cross Abstract: Forecasting the evolution of complex dynamical systems remains a fundamentally challenging task, primarily due to pronounced nonlinear interactions, high-dimensional state spaces, and the concomitant requirement for rigorous and r…

  2. arXiv stat.ML TIER_1 English(EN) · Gianluigi Rozza ·

    用于大规模连续动力学系统推断的二次阶降阶——高斯过程常微分方程框架

    Forecasting the evolution of complex dynamical systems remains a fundamentally challenging task, primarily due to pronounced nonlinear interactions, high-dimensional state spaces, and the concomitant requirement for rigorous and reliable uncertainty quantification. Contemporary r…

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